History-matching methodology that minimizes non-uniqueness problem and ensure smooth transition from history to prediction

ABSTRACT

Systems and methods include a computer-implemented method for predicting values. A numerical simulation model is generated based on observed production rates and flowing pressure and build-up (FPBU) test rates. A simulated diagnostic plot is generated using simulated FPBU data extracted from the numerical simulation model. Simulation model properties of the numerical simulation model are adjusted until the simulated diagnostic plot matches within a tolerance to an observed FPBU diagnostic plot. Predicted values including a static pressure, a water cut, and a gas-oil rate (GOR) are predicted using the simulated FPBU data. Observed data of a reservoir is reviewed and quality-checked based on comparing the predicted values within a tolerance of the observed data.

TECHNICAL FIELD

The present disclosure applies to history-matching of reservoirpermeability.

BACKGROUND

Permeability calibration using pressure transient derivatives istypically performed for single well models. As a result, whilepermeability can be calibrated, a current reservoir pressure cannot becalibrated on such models since the field-wide depletion and possibleinjection history from other wells is not usually incorporated into suchsingle-well models. In addition, the use of pressure transientderivative for single-well models' calibration cannot be used tocalibrate current well productivity index (PI). This is because theobserved delta-P on a well is a sum of delta-P due to the well's ownrate history and additional delta-P due to the superposition effect ofsurrounding wells in the same reservoir. Since these other wells are notusually included in the single well calibration model, the simulateddelta-P may be different from the actual delta-P, resulting in anincorrect calculation of PI. When history-matching is used and is drivenby single-point static pressure data, it is typically necessary toconduct a PI calibration at the end of history-match. This process canbe time-consuming, which further can downgrade confidence in the qualityof the model and can reduce the predictive capability of the model,especially with respect to infill wells. Performance of new wells in themodel may not be consistent with reality because the model has beenhistory-matched with property sets that are different from the truth.

SUMMARY

The present disclosure describes techniques that can be used todetermine reservoir permeability by history matching flowing pressureand build-up (FPBU) data.

In some implementations, a computer-implemented method includes thefollowing. A numerical simulation model is generated based on observedproduction rates and FPBU test rates. A simulated diagnostic plot isgenerated using simulated FPBU data extracted from the numericalsimulation model. Simulation model properties of the numericalsimulation model are adjusted until the simulated diagnostic plotmatches within a tolerance to an observed FPBU diagnostic plot.Predicted values including a static pressure, a water cut, and a gas-oilrate (GOR) are predicted using the simulated FPBU data. Observed data ofa reservoir is reviewed and quality-checked based on comparing thepredicted values within a tolerance of the observed data.

The previously described implementation is implementable using acomputer-implemented method; a non-transitory, computer-readable mediumstoring computer-readable instructions to perform thecomputer-implemented method; and a computer-implemented system includinga computer memory interoperably coupled with a hardware processorconfigured to perform the computer-implemented method, the instructionsstored on the non-transitory, computer-readable medium.

The subject matter described in this specification can be implemented inparticular implementations, so as to realize one or more of thefollowing advantages. The quality of a history-matched model can beimproved by minimizing the impact of non-uniqueness and ensuring thatthe history-matched model is a better representation of a reservoir.History-matching that is FPBU-driven can be used to reduce or eliminatethe non-uniqueness problem without requiring a time-consuming practiceof productivity index (PI) calibration, since permeability and PI can beindependently calibrated. For example, using the techniques of thepresent disclosure, permeability can be calibrated from derivativestabilization, and the PI can be calibrated based on a separationbetween a delta-P plot and the stabilization of the derivative plot. AnFPBU plot can help to calibrate an aquifer strength. History-matchedmodel properties can be close to unknown properties of the actualreservoir, eliminating the need to complete well PI calibration at theend of history-matching. In addition, this history-matching approach canfacilitate the identification of incorrect single-point static pressuresand identify missing well events such as inaccurate or missingproduction data. Performing individual calibration of parameters used inhistory-matching can ensure that non-uniqueness is reduced. Thishistory-matching approach can help to identify incorrect single-pointstatic pressures and identify missing well events such as inaccurate ormissing production data. Traditionally, history matching is an inverseproblem in which model permeability is updated until model responsematches with observed pressure. This traditional approach is atime-consuming trial-and-error process. The presently disclosed approachis much faster as it determines model permeability directly fromobserved FPBU.

The details of one or more implementations of the subject matter of thisspecification are set forth in the Detailed Description, theaccompanying drawings, and the claims. Other features, aspects, andadvantages of the subject matter will become apparent from the DetailedDescription, the claims, and the accompanying drawings.

DESCRIPTION OF DRAWINGS

FIG. 1A is an illustration showing an example of a graph of flowingpressure and build-up (FPBU) pressure transient data, according to someimplementations of the present disclosure.

FIG. 1B is a block diagram showing examples of differences in aconventional workflow versus an improved workflow, according to someimplementations of the present disclosure.

FIGS. 2A and 2B are plots collectively showing an example of a build-uppressure methodology, according to some implementations of the presentdisclosure.

FIG. 3 is an illustration showing an example of a hypothetical model andobserved single-point static pressures, according to someimplementations of the present disclosure.

FIG. 4 is an illustration showing an example of geo-model realization oftruth case having pessimistic permeability and non-matching pressure,according to some implementations of the present disclosure.

FIGS. 5A and 5B are plots collectively showing an example of matching ofthe geo-model realization pressure with properties different from truthcase, according to some implementations of the present disclosure.

FIG. 6 is an illustration showing a prediction result of static pressurematched geo-model, according to some implementations of the presentdisclosure.

FIG. 7 is an illustration showing an example of a history match forecastdiscontinuity and productivity index (PI) calibration, according to someimplementations of the present disclosure.

FIGS. 8A and 8B collectively illustrate an example of a hypotheticaltruth case production rate and pressure history, according to someimplementations of the present disclosure.

FIGS. 9A and 9B are plots collectively showing an example of a datasetto be matched based on a methodology for FPBU conducted on truth model,according to some implementations of the present disclosure.

FIG. 10 is a graph showing an example of a simulated pressure from ageo-model compared to observed static pressure, according to someimplementations of the present disclosure.

FIGS. 11A and 11B are plots collectively showing an example of ahistory-matching of observed FPBU using a geo-model, according to someimplementations of the present disclosure.

FIG. 12 is an illustration showing an example of a prediction comparisonbetween truth case and a history-matched case, according to someimplementations of the present disclosure.

FIGS. 13A and 13B are plots collectively showing an example of anFPBU-driven history-matching of a geo-model having, according to someimplementations of the present disclosure.

FIG. 14 is an illustration showing an example of a forecast comparison,according to some implementations of the present disclosure.

FIGS. 15A and 15B are plots collectively showing an example of flowrateand pressure history of hypothetical truth model, according to someimplementations of the present disclosure.

FIG. 16 is an illustration showing an example of a mismatch between ageo-model pressure and an observed static pressure, according to someimplementations of the present disclosure.

FIG. 17 is an illustration showing an example of a history-match of amodel with properties different than the truth model, according to someimplementations of the present disclosure.

FIG. 18 is an illustration showing an example of forecast profiles foran original synthetic model and a history-matched geo-model, accordingto some implementations of the present disclosure.

FIGS. 19A and 19B are plots collectively showing an example of anapplication of a proposed methodology to history-match the 10 mdgeo-model, according to some implementations of the present disclosure.

FIGS. 20A and 20B are plots collectively showing an example of anapplication of proposed methodology to ensure a history-match that isconsistent with truth case properties, according to some implementationsof the present disclosure.

FIG. 21 is an illustration showing an example of forecast profiles fortruth case forecast plot and history-matched case plot driven by staticpressure and FPBU, according to some implementations of the presentdisclosure.

FIG. 22 is an illustration showing an example of an impact of a well PIcalibration, according to some implementations of the presentdisclosure.

FIG. 23 is a flowchart of an example of a method for predicting valuesof static pressure, water cut, and gas-oil rate (GOR) using simulatedFPBU data, according to some implementations of the present disclosure.

FIG. 24 is a block diagram illustrating an example computer system usedto provide computational functionalities associated with describedalgorithms, methods, functions, processes, flows, and procedures asdescribed in the present disclosure, according to some implementationsof the present disclosure.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

The following detailed description describes techniques for conductinghistory matching using measured flowing pressure and build-up (FPBU) asthe objective function rather than the traditional use of datumpressure. The history matched model can then be used for reliableforecast of static pressure, water cut, and gas-oil rate (GOR). Variousmodifications, alterations, and permutations of the disclosedimplementations can be made and will be readily apparent to those ofordinary skill in the art, and the general principles defined may beapplied to other implementations and applications, without departingfrom scope of the disclosure. In some instances, details unnecessary toobtain an understanding of the described subject matter may be omittedso as to not obscure one or more described implementations withunnecessary detail and inasmuch as such details are within the skill ofone of ordinary skill in the art. The present disclosure is not intendedto be limited to the described or illustrated implementations, but to beaccorded the widest scope consistent with the described principles andfeatures.

Techniques of the present disclosure can be used to improve the qualityof a history-matched model by minimizing the impact of non-uniquenessand ensuring that the history-matched model is a better representationof a reservoir. As a result of the non-uniqueness problem that is commonto typical history-matching, it is sometimes necessary to carry outproductivity index (PI) calibration after history-matching, which can bea cumbersome process. The techniques of the present disclosure can beused in history-matching based on both flowing pressure (FP)+build-up(BU) derivative using a full-field simulation model. The derivativeportion helps to properly calibrate permeability and PI, while the FPportion helps to properly calibrate the aquifer strength, bottom-holeflowing pressure and wellbore skin. The individual determination of thevarious parameters helps to eliminate the need for trial-and-error andto eliminate/reduce the non-uniqueness problem.

The techniques of the present disclosure can also help to detectincorrect single-point pressure data and missing well event data. Oncethe FPBU data has been matched, the model can predict any futuresingle-point pressure data if and only if: 1) all relevant well eventssuch as depletion and injection are captured in the model, and 2) theobserved pressure record is correct. If history-matching is conductedbased on the techniques of the present disclosure, there is no need forPI calibration at the end of history-matching, thereby saving time andimproving confidence in the quality of the resulting history-matchedmodel.

As shown in FIG. 12 and FIG. 18, when a history-match suffers fromnon-uniqueness problem such that the properties of the history-matchedmodel are not similar to the properties of the truth model, then duringforecast, the productivity of a history-matched model may not be similarto that of the truth model. Practitioners of history-matching typicallyuse PI calibration to increase the productivity of the well posthistory-matching.

A PI multiplier can be applied on the static-pressure drivenhistory-matching on a trial-and-error basis until a satisfactory matchof PI is achieved. FIG. 22 shows a prediction result after anapplication of the PI multiplier=10 on the history-matched model shownin FIG. 18.

The challenge of the PI multiplier is that, even if the start-up PI ismatched at the beginning of the forecast, the long-term performanceprofile of the well is not necessarily similar to the truth case model.This is evident in the decline rates of the plots in FIG. 22. Otherchallenges and limitations of the practice of PI calibration caninclude: (a) being a time-consuming process, (b) downgrading confidencein the quality of the history-matched model, and (c) reducing thepredictive capability of the model, especially with regards to infill ornew wells production forecasting.

History-matching is the process of tuning the properties of a geo-modeluntil its dynamic results mimic observed data. Observed data that ishistory-matched includes generally static pressure data and, ifavailable, production ratios such as water-cut and GOR, modular dynamictesting (MDT), mobility, and pulsed-neutron well logging (PNL). A firststage of history-matching can include a static pressure match. Wherethere is no multiphase flow, and where other less readily availablereservoir characterization data are unavailable, a static pressure matchmay be the only match conducted.

Geo-model properties that are tuned can include, for example,permeability, reservoir connectivity pattern (facies map), and aquiferstrength. Well parameters that are tuned during history-matching caninclude the PI.

Non-uniqueness can arise from the fact that several combinations ofparameters can result in an equiprobable history-matched model, each ofwhich potentially resulting in different prediction (forecast) profiles.It is generally recognized that any single deterministic history-matchcan produce significant non-uniqueness resulting from multiple potentialdegrees-of-freedom for history-matching. Techniques of the presentdisclosure include a new methodology to conduct history-matching thatcan minimize the occurrence of non-uniqueness in history-matching,resulting in a reduction of the associated forecast uncertainties.

History-matching is typically performed using oil rate constraints,while prediction is performed using pressure constraints. Thisdifference in the use of constraints from oil-rate to pressure at thestart of prediction causes a discontinuity in well performance which istraditionally corrected using PI calibration. A delay can occur whenmoving from a well-rate-controlled history-match mode to awell-pressure-controlled prediction. Wells exhibiting too greatpotential-well PI's are typically adjusted to correct this problem. Whenhistory-matching is conducted according to techniques of the presentdisclosure, the discontinuity at the history-forecast junction can beminimized, eliminating the need for well PI calibration as shown in FIG.21

Definition of Terms

Flowing pressure and build-up (FPBU) is a pressure transient survey inwhich a measuring gauge is lowered into a well while it is flowing. Oncethe gauge reaches the perforations, bottom-hole flowing pressure ismeasured for some time, the well is shut-in, and then the shut-inpressure is measured.

Observed single-point static pressure is defined as the last-measureddata point of an FPBU before the gauge is disengaged and retrieved, orat times a gauge is lowered into an already shut-in well to measure onlythe shut-in pressure. The single-point static pressure is likewise thefinal measured data before the gauge is retrieved.

Connected reservoir regions (CRRsP refers to spatial regions of areservoir that are in instantaneous pressure communication. Suchinstantaneous communication is an indication of comparable levels ofpermeability.

FIG. 1A is an illustration showing an example of a graph 100 of FPBUpressure transient data, according to some implementations of thepresent disclosure. The graph 100 includes a flowing period 102 and abuild-up period 104, relative to time. The graph 100 is graphed relativeto a pressure 106 and a flowrate 108.

The motivation for the techniques of the present disclosure is toconduct history-matching in a way that eliminates/reduces thenon-uniqueness problem. An example of the methodology is illustrated inFIG. 1A and includes using FPBU data for history-matching instead of theconventional approach of using single-point static pressure data. FPBUstands for flowing pressure and build up. This is acquired through welltesting and an example is illustrated in FIG. 2. Using this methodology,it is required to history-match (HM) the build-up (BU) derivative, theBU delta-Pressure (dP), as well as the flowing pressure (FP) plot.

FIG. 1B is a block diagram 150 showing examples of differences in aconventional workflow 152 versus an improved workflow 154, according tosome implementations of the present disclosure. Inputs to the workflowsincludes an observed multi-well datum static pressure 156, observedmulti-well production ratios 158 (including at least water cut and gasoil rate (GOR)), observed multi-well production data 160 (including atleast oil and liquid rates), and observed multi-well FPBU data 162. Ageological model 164 can be built using cored data (including porosityand permeability data) and logs, and the model is used in both workflows152 and 154.

In the conventional workflow 152, a numerical simulation 166 model iscreated that is constrained to an observed production rate. A comparison168 is made of numerical static pressure, water cut, and a GOR to anobserved equivalent. The results of the comparison are compared 170within a tolerance. If the comparison is not within the tolerance, thengeological model properties (such as permeability, aquifer strength,spatial connectivity) are reviewed 172, and the conventional workflow152 is updated.

In the improved workflow 154, a numerical simulation model is created174 that is constrained to an observed production rate and FPBU testrates. At 176, simulated FPBU data is extracted and a diagnostic plot iscreated. At 178, simulation model properties are adjusted until asimulated diagnostic plot matches with an observed FPBU diagnostic plot.At 180, static pressure, water cut, and the GOR are predicted. Acomparison 182 is made to see if the predicted values are within atolerance. If not, at 184, the observed data is reviewed andquality-checked.

FIGS. 2A and 2B are plots collectively showing an example of a build-uppressure methodology, according to some implementations of the presentdisclosure. FIG. 2A shows a history plot 200 that includes a glowingpressure 202 and a build-up 204 plotted relative to a pressure 206 (forexample, in pounds per square inch, absolute (psia)). FIG. 2B shows alog-log plot 250 with a BU delta-P 252 and a BU pressure derivative 254that are plotted relative to log axes including a time 256 (for example,in hours (hr)) and a pressure 258 (for example, in pounds per squareinch (psi)).

After history-matching is performed, the permeability multipliersrequired at each FPBU well location are then used to calibrate theproperties of the non-well grid-blocks within each connected reservoirregions (CRR). This step would ensure that not only is the well itselfcalibrated (and will thus have a smooth transition from history toforecast without need for well PI calibration), but also the inter-wellareas within each CRR would also be calibrated and would ensure thereliability of infill well prediction results.

The Non-Uniqueness Problem

The non-uniqueness problem derives from the practice of using observedsingle-point static pressure as the key criteria for history-matching asexplained with the following example.

FIG. 3 is an illustration 300 showing an example of a hypothetical model302 and observed single-point static pressures 304 a and 304 b,according to some implementations of the present disclosure. Theillustration 300 shows a hypothetical model where the reservoir has apermeability of 200 mD (millidarcies) and aquifer transmissibility of 1.The production profile and observed single-point static pressuresrecorded on a well in the reservoir are as shown in FIG. 3. Theinformation in the illustration 300 is plotted relative to dates 306,pressures 308, and a bottom hole pressure (BHP) 310. Duringgeo-modelling of this reservoir, if the model permeability is notproperly captured, and is given as 100 md, by using an oil rateconstraint for history-matching, the model is able to produce therequired rate, although it is not able to match its observed staticpressures.

FIG. 4 is an illustration 400 showing an example of geo-modelrealization of truth case 402 having pessimistic permeability andnon-matching pressure, according to some implementations of the presentdisclosure. In this example, in order to match the observed staticpressure, aquifer transmissivity and volume are increased.

FIGS. 5A and 5B are plots collectively showing an example of matching ofthe geo-model realization pressure with properties different from truthcase, according to some implementations of the present disclosure. Aplot 502 is produced using an observed static pressure as the criteriafor history-matching, allowing the achievement of both a production rateand an observed static pressure matching using properties that aredifferent from the properties of the original hypothetical model shownin FIG. 3.

FIG. 6 is an illustration showing a prediction result of static pressurematched geo-model, according to some implementations of the presentdisclosure. The history-match scenario having wrong characterization(lesser permeability than truth) is shown in FIG. 6, now extended in aforecast mode using a BHP 310=4365 psia. FIG. 6 shows a historicalflow-rate 602 from the truth case, a historical bottom-hole flowingpressure 604 from the truth case, and a rate forecast 606 of pessimisticmodel at start of forecast. Note that the BHP 310 at end-of-history inthe hypothetical truth case is 4365 psia. As shown in the predictionresults of FIG. 6, the pressure history-matched well could not sustain500 barrels per day (bbl/d) production during prediction. Again, asstated earlier, this is an indication of a history match that has beenobtained based on a different characterization than the originalhypothetical model. History-matching can be an essential but tediousprocess in reservoir simulation. This process can be complex andtime-consuming, and can result in many degrees of freedom. Usually,after a period of laborious history match exercise, it is sometimes notpractical to re-visit the history match assumptions, it is customary touse a PI multiplier to alleviate the potential uncertainties related towell mismatches. Well-level matching can be particularly lengthy forlarge oil fields with hundreds or thousands of completions. Short-termforecast confidence can be improved by compensating well-levelmismatches observed at the end of history-matching with PI multiplierswithout solving the inherent model problems.

If a PI multiplier of 2 is applied to the well, a sudden spike can beobserved in the oil rate, which rapidly declines as shown by line 704 inFIG. 7. An additional step that practitioners can use in order toprevent the spike caused by using PI multiplier is to constrain the wellat the required production rate, in this case maximum oil rate=500. Thewell then produces the required rate as shown by line 708 in FIG. 7.

FIG. 7 is an illustration showing an example of a history match forecastdiscontinuity and PI calibration 700, according to some implementationsof the present disclosure. FIG. 7 shows a forecast flow rate 702 ofpessimistic history-matched model before PI multiplier, forecast flowrate 704 of pessimistic history-matched model after PI multiplier, and ahistorical flow rate 706 from truth case model. In the disclosedmethodology, history-matching is based on FPBU rather than single-pointstatic pressure as shown in FIGS. 2A and 2B and described in detailusing two scenarios.

Scenario 1

FIGS. 8A and 8B collectively illustrate an example of a hypotheticaltruth case production rate 800 and pressure history 850, according tosome implementations of the present disclosure. To achieve this, asynthetic model having uniform permeability of 200 md, bottom aquiferdrive and other properties was created and subjected to the flow-rateconstraint shown in FIG. 8A. A production rate history 802 is plottedrelative to time 804 (for example, in days) and a production rate 806(for example, in standard barrels per day (std/bpd)). In the pressurehistory 850 of FIG. 8B, a corresponding pressure transient 808 isplotted relative to time 804 and a pressure (P) 810 (for example, inpsia). The model represented in FIGS. 8A and 8B is taken as ahypothetical truth model, and hence the simulated pressure is taken asfield-observed (measured) data. FIG. 8B includes an FPBU 812 andsingle-point static pressures 814. An FPBU survey was conducted asshown, and the relevant plots for this FPBU are shown in FIGS. 9A and9B.

FIGS. 9A and 9B are plots 900 and 950 collectively showing an example ofa dataset to be matched based on a methodology for FPBU conducted ontruth model, according to some implementations of the presentdisclosure. Graph 900 shows a measured FPBU (observed data 902) and isplotted relative to time 906 and a pressure 908. During the geo-modelingof this “truth reservoir,” the permeability model was underestimated,resulting in a model having a permeability of 70 md. In FIG. 9B, graph950 includes plots for an observed derivative 952, an observed delta-P(pressure) 954, a simulated derivative 956, and a simulated delta-P 958.Plots in FIG. 9B are plotted relative to a log-scale time 960 and alog-scale pressure 962.

FIG. 10 is a graph 1000 showing an example of a simulated pressure 1002from a 70 md geo-model compared to observed static pressure, accordingto some implementations of the present disclosure. FIG. 10 shows a matchof simulated shut-in pressure and observed shut-in pressure (observedshut-in pressure is the shut-in pressures simulated on the hypotheticaltruth reservoir model). The simulated and observed shut-in pressuresappear to match, even though the geo-model permeability is differentfrom the truth model permeability (because of the non-uniquenessproblem). However, by using the methodologies of the present disclosure,it is possible to avoid non-uniqueness as shown in the FIGS. 11A and11B.

FIGS. 11A and 11B are plots collectively showing an example of ahistory-matching of observed FPBU using a 70 md geo-model, according tosome implementations of the present disclosure. A model whose pressurematch results appear to match when assessed from the prism of staticpressure match may suddenly not match when viewed in the prism of themethodologies of the present disclosure. Fundamental issues that can beobserved from the FPBU match shown in FIGS. 11A and 11B include thefollowing. First, a simulated flowing pressure is too low compared tothe truth case. At least 3 parameters can be tuned to correct this: a)increased aquifer support, b) increased well productivity index forexample, introduce negative wellborn skin (implying simulation, forexample, through fracking or acid simulation), and c) increasedpermeability. Second, the end-point of the simulated and observed FPBUpressure are not matched. For example, even the shape of the simulatedbuild-up is not similar to the shape of the observed data build-up.Reservoir permeability and skin are the key influences of the shape ofpressure build-up. Third, the BU-derivative of observed and simulated BUare not matching. Permeability is the controlling factor for thederivative stabilization. Fourth, the BU-delta-P is not matching.Formation skin damage (well PI) is the controlling factor for thisparameter.

Techniques of the present disclosure used for history-matching canreduce the uncertainties involved in deterministic history-matching byproviding diagnostic plots that help to constrain some of the degrees offreedom. Uncertainties can be reduced in the following ways. First, theincorporation of derivative plot can help to fix the value ofpermeability, because the derivative stabilization is exclusively afunction of permeability. Second, the incorporation of thedelta-pressure plot helps to fix the value of skin (well productivityindex), because the separation between the derivative stabilization andthe delta-pressure plot is exclusively a function of skin. Having fixedthe values of formation skin damage and permeability, any remainingmismatch can be approached using other parameters such as aquiferstrength and reservoir connectivity.

The use of a static pressure visualization in FIG. 10 can be interpretedas already having a history-matched model. The model of FIG. 10 can beused to predict the truth case using a BHP control of 4874 psi (forexample, the BHP at beginning of prediction in the truth case is 4874psi). FIG. 12 shows two key observations that the production forecastrate of the history-matched model is not the same as the productionforecast rate of the truth model. For example, there is a discontinuityof production rates between the history and the forecast in thehistory-matched case, but a smooth continuity in the truth case. Thisdiscontinuity is traditionally corrected by using PI calibration withoutreally addressing its cause. Discontinuity refers to the initialforecast rate being different from the final historical rate when thefinal historical BHP is used as the forecast constraint.

FIG. 12 is an illustration showing an example of a prediction comparison1200 between truth case 1202 and a history-matched case 1204 (forexample, a geo-model with 70 md), according to some implementations ofthe present disclosure. Plots of the prediction comparison 120 areplotted relative to a date 1206 and a production rate 1208.

Based on the use of history-matching, FIGS. 11A and 11B identify a needto increase permeability (noting that a derivative stabilization valueis inversely proportional to permeability). For example, by applying apermeability multiplier of 2.5 on the model, FIGS. 13A and 13B areobtained.

FIGS. 13A and 13B are plots collectively showing an example of anFPBU-driven history-matching of the geo-model having 70 md, according tosome implementations of the present disclosure. Graph 1300 includes anobserved data plot 1302, a simulation data plot 1304 that are plottedrelative to time 106 and a pressure 1308. Graph 1350 includes anobserved data derivative 1352, a delta-P observed plot 1354, a simulateddata derivative 1356, a delta-P simulated plot 1358, plotted relative toa log-log graph including time 1360 and pressure 1362.

A permeability multiplier (for example, 2.5 in this case) can improvethe permeability match, the well productivity match, the match of theflowing pressure, and the match of the shape of build-up. The techniquesof the present disclosure can be used to conduct history-matching withfeatures that help to minimize the solution non-uniqueness. An exampleforecast result of this FPBU driven history-matched model is shown inFIG. 14.

FIG. 14 is an illustration showing an example of a forecast comparison,according to some implementations of the present disclosure. Forecast1402 based on FPBU history-matching, a truth case forecast 1404, an aforecast 1406 based on a static pressure driven history matching 1406,all plotted relative to time 1408 and a production rata 1410. Theforecast production rate is consistent with the truth case, and thetransition from the HM to forecast is near smooth. A limiteddiscontinuity that is observed is because the flowing pressure is notperfectly matched. For example, as shown in FIGS. 13A and 13B, aslightly greater permeability multiplier of 2.8 can lead to matching allparameters and can ensure a better smoothness from HM to forecastwithout any need for PI calibration.

Scenario 2

A synthetic model having uniform permeability of 200 md, edge aquiferdrive and other properties was created. The flow-rate constraint and thecorresponding pressure transient is shown in FIGS. 15A and 15B. Thefinal shut-in pressure of the FPBU conducted in January-2021 is taken asthe ‘truth’ observed pressure data

FIGS. 15A and 15B are plots collectively showing an example of flowrateand pressure history of hypothetical truth model, according to someimplementations of the present disclosure. A production rate plot 1502is plotted relative to a date 1504 and a production rate 1506. Apressure plot 1552 is plotted relative to a data 1554 and a pressure1556. A segment 1558 of the 1552 is attributable to FPBU 1558.

During the geo-modeling of the synthetic reservoir, the resultinggeo-model permeability was underestimated, resulting in a model havingpermeability of 10 md. FIG. 16 shows the history-match result of thegeo-model, using the static pressure driven approach (observed staticpressure is obtained from the truth model)

FIG. 16 is an illustration showing an example of a mismatch 1600 between10 md geo-model pressure 1604 and an observed static pressure 1602,according to some implementations of the present disclosure. Plots 1602and 1604 are plotted relative to time 1606 and a production rate 1608.In order to improve a history-match of the geo-model using a staticpressure driven approach, multiple scenarios are possible. As anexample, an excellent pressure match of FIG. 17 can be obtained usingpermeability multiplier of 4.5 and multiplier of aquifer volume of 1.3.This again is another example of the non-uniqueness problem, and it ispossible to achieve a history-match of pressure using differentproperties than are in the original synthetic model.

FIG. 17 is an illustration showing an example of a history-match 1700 ofa 10 md model 1702 with properties different than the truth model 1602,according to some implementations of the present disclosure. Someapplications of a simulation model can include determining the potentialproduction rates of wells under various operating conditions. Assumethat operators want to invest in a surface facilities upgrade that wouldallow for the production at a lesser BHP than, for example, 5000 psi. Byimposing a minimum BHP target of 5000 psi on the history-matched modeland comparing the production profile to that of the truth case, it canbe seen that the history-matched model has a different profile comparedto the truth case.

FIG. 18 is an illustration showing an example of forecast profiles 1800for an original synthetic model 1802 and the history-matched 10 mdgeo-model 1702, according to some implementations of the presentdisclosure. The history-matched case has a different forecast than thetruth, considering that the history-match was achieved usingpermeability multiplier of 4.5 (corresponding to 4.5*10=45mdpermeability), while the truth case is based on 200 md. The match hadbeen obtained by using aquifer size to compensate for what is missing inpermeability.

The FPBU history-matching approach can be used to assist thehistory-matching as follows. By matching the FPBU data rather than justthe static-pressure data (the static-pressure data is the last measuredpressure of the FPBU data), it becomes evident that the model ispessimistic in permeability as shown in FIGS. 19A and 19B.

FIGS. 19A and 19B are plots 1900 and 1950 collectively showing anexample of an application of a proposed methodology to history-match the10 md geo-model, according to some implementations of the presentdisclosure. Plot 1900 includes an observed data plot 1902 and asimulated data plot 1904. Both plots 1902 and 1904 are plotted relativeto time 1906 and a production rate 1908. Plot 1950 includes an observedderivative plot 1952, a delta-P observed plot 1954, a simulatedderivative plot 1956, and a simulated delta-P plot 1958. The plots 1952,1954, 1956, and 1959 are plotted relative to a log time 1960 and a logproduction rate 1962.

A first step of history-matching can be to ensure a match of pressurederivative of the model and the truth. In some implementations, therequired permeability multiplier can be calculated using Equation (1):

$\begin{matrix}{{multKx} = {\frac{{derivative}{stabilization}{of}{model}}{{derivative}{stabilization}{of}{truth}} \approx \frac{70}{4} \approx 18}} & (1)\end{matrix}$

FIGS. 20A and 20B are plots 2000 and 2050 collectively showing anexample of an application of proposed methodology to ensure ahistory-match that is consistent with truth case properties, accordingto some implementations of the present disclosure. Graph 2000 shows theobserved data plot 1902 and the simulated data plot 1904. Graph 2050shows an observed derivative plot 1952, an observed delta-P plot 1954, asimulated derivative plot 1956 and a simulated delta-P plot 1958. Theplots 1952, 1954, 1956, and 1958 are plotted relative to a log date axis1960 and a log pressure axis 1962.

As shown in FIGS. 20A and 20B, after a permeability multiply of 18(corresponding to 18*10=180md permeability), the permeability is almostperfectly matching as well as the flowing pressure. There is no furtherneed to change aquifer parameters.

FIG. 21 is an illustration showing an example of forecast profiles fortruth case forecast plot 2106 and history-matched case plot 2102 and2104 driven by static pressure and FPBU, according to someimplementations of the present disclosure. Graph 2100 includesFPBU-driven history-matching forecast plot 2102, static pressure-drivenhistory-matching forecast plot 2104, and the truth case forecast plot2106. The plots 2102, 2104, and 2106 are plotted relative to time 2108(for example, in days) and production rate 2110 (for example, instb/day).

FIG. 22 is an illustration showing an example of an impact of a well PIcalibration, according to some implementations of the presentdisclosure. If a production forecast is made using a BHP minimum of 5000psia, for example as shown in FIG. 21, then the history-match based onFPBU provides very similar results to the original synthetic model.Graph 2200 includes PI-calibrated history-matched case forecast plot2202.

FIG. 23 is a flowchart of an example of a method 2300 for predictingvalues of static pressure, water cut, and GOR using simulated FPBU data,according to some implementations of the present disclosure. For clarityof presentation, the description that follows generally describes method2300 in the context of the other figures in this description. However,it will be understood that method 2300 can be performed, for example, byany suitable system, environment, software, and hardware, or acombination of systems, environments, software, and hardware, asappropriate. In some implementations, various steps of method 2300 canbe run in parallel, in combination, in loops, or in any order.

Method 2300 uses a forward approach to history matching, where reservoirproperties of a reservoir are determined explicitly, independent offield performance data. For example, reservoir properties areforward-determined by history-matching FPBU data. Once reservoirproperties have been determined, reservoir responses (field performancedata) are then predicted. Any error in prediction of a reservoirresponse can be interpreted as uncertainty or an error in datameasurement, providing a quality check of the measured data.

At 2302, a numerical simulation model is generated based on observedproduction rates and FPBU test rates. For example, models that createoutputs 904 or 1904 can be generated. From 2302, method 2300 proceeds to2304.

At 2304, a simulated diagnostic plot is generated using simulated FPBUdata extracted from the numerical simulation model. For example, thesimulation outputs 904 or 1904 can be generated based on FPBUhistory-matching, and used to create diagnostic plots 958 or 1956. From2304, method 2300 proceeds to 2306.

At 2306, simulation model properties of the numerical simulation modelare adjusted until the simulated diagnostic plot matches within atolerance to an observed FPBU diagnostic plot. Example adjustments arefrom plot 1956 of FIG. 19 to plot 1956 of FIG. 20, for example. From2306, method 2300 proceeds to 2308.

At 2308, predicted values including a static pressure, a water cut, anda GOR are predicted using the simulated FPBU data. From 2308, method2300 proceeds to 2310.

At 2310, observed data of a reservoir is reviewed and quality-checkedbased on comparing the predicted values within a tolerance of theobserved data. After 2310, method 2300 can stop.

FIG. 24 is a block diagram of an example computer system 2400 used toprovide computational functionalities associated with describedalgorithms, methods, functions, processes, flows, and proceduresdescribed in the present disclosure, according to some implementationsof the present disclosure. The illustrated computer 2402 is intended toencompass any computing device such as a server, a desktop computer, alaptop/notebook computer, a wireless data port, a smart phone, apersonal data assistant (PDA), a tablet computing device, or one or moreprocessors within these devices, including physical instances, virtualinstances, or both. The computer 2402 can include input devices such askeypads, keyboards, and touch screens that can accept user information.Also, the computer 2402 can include output devices that can conveyinformation associated with the operation of the computer 2402. Theinformation can include digital data, visual data, audio information, ora combination of information. The information can be presented in agraphical user interface (UI) (or GUI).

The computer 2402 can serve in a role as a client, a network component,a server, a database, a persistency, or components of a computer systemfor performing the subject matter described in the present disclosure.The illustrated computer 2402 is communicably coupled with a network2430. In some implementations, one or more components of the computer2402 can be configured to operate within different environments,including cloud-computing-based environments, local environments, globalenvironments, and combinations of environments.

At a top level, the computer 2402 is an electronic computing deviceoperable to receive, transmit, process, store, and manage data andinformation associated with the described subject matter. According tosome implementations, the computer 2402 can also include, or becommunicably coupled with, an application server, an email server, a webserver, a caching server, a streaming data server, or a combination ofservers.

The computer 2402 can receive requests over network 2430 from a clientapplication (for example, executing on another computer 2402). Thecomputer 2402 can respond to the received requests by processing thereceived requests using software applications. Requests can also be sentto the computer 2402 from internal users (for example, from a commandconsole), external (or third) parties, automated applications, entities,individuals, systems, and computers.

Each of the components of the computer 2402 can communicate using asystem bus 2403. In some implementations, any or all of the componentsof the computer 2402, including hardware or software components, caninterface with each other or the interface 2404 (or a combination ofboth) over the system bus 2403. Interfaces can use an applicationprogramming interface (API) 2412, a service layer 2413, or a combinationof the API 2412 and service layer 2413. The API 2412 can includespecifications for routines, data structures, and object classes. TheAPI 2412 can be either computer-language independent or dependent. TheAPI 2412 can refer to a complete interface, a single function, or a setof APIs.

The service layer 2413 can provide software services to the computer2402 and other components (whether illustrated or not) that arecommunicably coupled to the computer 2402. The functionality of thecomputer 2402 can be accessible for all service consumers using thisservice layer. Software services, such as those provided by the servicelayer 2413, can provide reusable, defined functionalities through adefined interface. For example, the interface can be software written inJAVA, C++, or a language providing data in extensible markup language(XML) format. While illustrated as an integrated component of thecomputer 2402, in alternative implementations, the API 2412 or theservice layer 2413 can be stand-alone components in relation to othercomponents of the computer 2402 and other components communicablycoupled to the computer 2402. Moreover, any or all parts of the API 2412or the service layer 2413 can be implemented as child or sub-modules ofanother software module, enterprise application, or hardware modulewithout departing from the scope of the present disclosure.

The computer 2402 includes an interface 2404. Although illustrated as asingle interface 2404 in FIG. 24, two or more interfaces 2404 can beused according to particular needs, desires, or particularimplementations of the computer 2402 and the described functionality.The interface 2404 can be used by the computer 2402 for communicatingwith other systems that are connected to the network 2430 (whetherillustrated or not) in a distributed environment. Generally, theinterface 2404 can include, or be implemented using, logic encoded insoftware or hardware (or a combination of software and hardware)operable to communicate with the network 2430. More specifically, theinterface 2404 can include software supporting one or more communicationprotocols associated with communications. As such, the network 2430 orthe interface's hardware can be operable to communicate physical signalswithin and outside of the illustrated computer 2402.

The computer 2402 includes a processor 2405. Although illustrated as asingle processor 2405 in FIG. 24, two or more processors 2405 can beused according to particular needs, desires, or particularimplementations of the computer 2402 and the described functionality.Generally, the processor 2405 can execute instructions and canmanipulate data to perform the operations of the computer 2402,including operations using algorithms, methods, functions, processes,flows, and procedures as described in the present disclosure.

The computer 2402 also includes a database 2406 that can hold data forthe computer 2402 and other components connected to the network 2430(whether illustrated or not). For example, database 2406 can be anin-memory, conventional, or a database storing data consistent with thepresent disclosure. In some implementations, database 2406 can be acombination of two or more different database types (for example, hybridin-memory and conventional databases) according to particular needs,desires, or particular implementations of the computer 2402 and thedescribed functionality. Although illustrated as a single database 2406in FIG. 24, two or more databases (of the same, different, orcombination of types) can be used according to particular needs,desires, or particular implementations of the computer 2402 and thedescribed functionality. While database 2406 is illustrated as aninternal component of the computer 2402, in alternative implementations,database 2406 can be external to the computer 2402.

The computer 2402 also includes a memory 2407 that can hold data for thecomputer 2402 or a combination of components connected to the network2430 (whether illustrated or not). Memory 2407 can store any dataconsistent with the present disclosure. In some implementations, memory2407 can be a combination of two or more different types of memory (forexample, a combination of semiconductor and magnetic storage) accordingto particular needs, desires, or particular implementations of thecomputer 2402 and the described functionality. Although illustrated as asingle memory 2407 in FIG. 24, two or more memories 2407 (of the same,different, or combination of types) can be used according to particularneeds, desires, or particular implementations of the computer 2402 andthe described functionality. While memory 2407 is illustrated as aninternal component of the computer 2402, in alternative implementations,memory 2407 can be external to the computer 2402.

The application 2408 can be an algorithmic software engine providingfunctionality according to particular needs, desires, or particularimplementations of the computer 2402 and the described functionality.For example, application 2408 can serve as one or more components,modules, or applications. Further, although illustrated as a singleapplication 2408, the application 2408 can be implemented as multipleapplications 2408 on the computer 2402. In addition, althoughillustrated as internal to the computer 2402, in alternativeimplementations, the application 2408 can be external to the computer2402.

The computer 2402 can also include a power supply 2414. The power supply2414 can include a rechargeable or non-rechargeable battery that can beconfigured to be either user- or non-user-replaceable. In someimplementations, the power supply 2414 can include power-conversion andmanagement circuits, including recharging, standby, and power managementfunctionalities. In some implementations, the power-supply 2414 caninclude a power plug to allow the computer 2402 to be plugged into awall socket or a power source to, for example, power the computer 2402or recharge a rechargeable battery.

There can be any number of computers 2402 associated with, or externalto, a computer system containing computer 2402, with each computer 2402communicating over network 2430. Further, the terms “client,” “user,”and other appropriate terminology can be used interchangeably, asappropriate, without departing from the scope of the present disclosure.Moreover, the present disclosure contemplates that many users can useone computer 2402 and one user can use multiple computers 2402.

Described implementations of the subject matter can include one or morefeatures, alone or in combination.

For example, in a first implementation, a computer-implemented methodincludes the following. A numerical simulation model is generated basedon observed production rates and FPBU test rates. A simulated diagnosticplot is generated using simulated FPBU data extracted from the numericalsimulation model. Simulation model properties of the numericalsimulation model are adjusted until the simulated diagnostic plotmatches within a tolerance to an observed FPBU diagnostic plot.Predicted values including a static pressure, a water cut, and a GOR arepredicted using the simulated FPBU data. Observed data of a reservoir isreviewed and quality-checked based on comparing the predicted valueswithin a tolerance of the observed data.

The foregoing and other described implementations can each, optionally,include one or more of the following features:

A first feature, combinable with any of the following features, wherethe tolerance is defined as 5%.

A second feature, combinable with any of the previous or followingfeatures, where the diagnostic plot is a derivative plot for stabilizinga derivative of a function of permeability.

A third feature, combinable with any of the previous or followingfeatures, where the diagnostic plot is a delta-pressure plot for fixingthe value of a well productivity index as a separation between aderivative stabilization and a function of skin.

A fourth feature, combinable with any of the previous or followingfeatures, where the diagnostic plot includes a plot for ahistory-matched pessimistic case plus a productivity index (PI)multiplier.

A fifth feature, combinable with any of the previous or followingfeatures, where the diagnostic plot includes a plot for ahistory-matched pessimistic case plus a PI multiplier and a rateconstraint.

A sixth feature, combinable with any of the previous or followingfeatures, where the diagnostic plot includes a truth case forecast and ahistory-matched case forecast.

In a second implementation, a non-transitory, computer-readable mediumstores one or more instructions executable by a computer system toperform operations including the following. A numerical simulation modelis generated based on observed production rates and FPBU test rates. Asimulated diagnostic plot is generated using simulated FPBU dataextracted from the numerical simulation model. Simulation modelproperties of the numerical simulation model are adjusted until thesimulated diagnostic plot matches within a tolerance to an observed FPBUdiagnostic plot. Predicted values including a static pressure, a watercut, and a GOR are predicted using the simulated FPBU data. Observeddata of a reservoir is reviewed and quality-checked based on comparingthe predicted values within a tolerance of the observed data.

The foregoing and other described implementations can each, optionally,include one or more of the following features:

A first feature, combinable with any of the following features, wherethe tolerance is defined as 5%.

A second feature, combinable with any of the previous or followingfeatures, where the diagnostic plot is a derivative plot for stabilizinga derivative of a function of permeability.

A third feature, combinable with any of the previous or followingfeatures, where the diagnostic plot is a delta-pressure plot for fixingthe value of a well productivity index as a separation between aderivative stabilization and a function of skin.

A fourth feature, combinable with any of the previous or followingfeatures, where the diagnostic plot includes a plot for ahistory-matched pessimistic case plus a productivity index (PI)multiplier.

A fifth feature, combinable with any of the previous or followingfeatures, where the diagnostic plot includes a plot for ahistory-matched pessimistic case plus a PI multiplier and a rateconstraint.

A sixth feature, combinable with any of the previous or followingfeatures, where the diagnostic plot includes a truth case forecast and ahistory-matched case forecast.

In a third implementation, a computer-implemented system includes one ormore processors and a non-transitory computer-readable storage mediumcoupled to the one or more processors and storing programminginstructions for execution by the one or more processors. Theprogramming instructions instruct the one or more processors to performoperations including the following. A numerical simulation model isgenerated based on observed production rates and FPBU test rates. Asimulated diagnostic plot is generated using simulated FPBU dataextracted from the numerical simulation model. Simulation modelproperties of the numerical simulation model are adjusted until thesimulated diagnostic plot matches within a tolerance to an observed FPBUdiagnostic plot. Predicted values including a static pressure, a watercut, and a GOR are predicted using the simulated FPBU data. Observeddata of a reservoir is reviewed and quality-checked based on comparingthe predicted values within a tolerance of the observed data.

The foregoing and other described implementations can each, optionally,include one or more of the following features:

A first feature, combinable with any of the following features, wherethe tolerance is defined as 5%.

A second feature, combinable with any of the previous or followingfeatures, where the diagnostic plot is a derivative plot for stabilizinga derivative of a function of permeability.

A third feature, combinable with any of the previous or followingfeatures, where the diagnostic plot is a delta-pressure plot for fixingthe value of a well productivity index as a separation between aderivative stabilization and a function of skin.

A fourth feature, combinable with any of the previous or followingfeatures, where the diagnostic plot includes a plot for ahistory-matched pessimistic case plus a productivity index (PI)multiplier.

A fifth feature, combinable with any of the previous or followingfeatures, where the diagnostic plot includes a plot for ahistory-matched pessimistic case plus a PI multiplier and a rateconstraint.

Implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Software implementations of the described subjectmatter can be implemented as one or more computer programs. Eachcomputer program can include one or more modules of computer programinstructions encoded on a tangible, non-transitory, computer-readablecomputer-storage medium for execution by, or to control the operationof, data processing apparatus. Alternatively, or additionally, theprogram instructions can be encoded in/on an artificially generatedpropagated signal. For example, the signal can be a machine-generatedelectrical, optical, or electromagnetic signal that is generated toencode information for transmission to a suitable receiver apparatus forexecution by a data processing apparatus. The computer-storage mediumcan be a machine-readable storage device, a machine-readable storagesubstrate, a random or serial access memory device, or a combination ofcomputer-storage mediums.

The terms “data processing apparatus,” “computer,” and “electroniccomputer device” (or equivalent as understood by one of ordinary skillin the art) refer to data processing hardware. For example, a dataprocessing apparatus can encompass all kinds of apparatuses, devices,and machines for processing data, including by way of example, aprogrammable processor, a computer, or multiple processors or computers.The apparatus can also include special purpose logic circuitryincluding, for example, a central processing unit (CPU), afield-programmable gate array (FPGA), or an application-specificintegrated circuit (ASIC). In some implementations, the data processingapparatus or special purpose logic circuitry (or a combination of thedata processing apparatus or special purpose logic circuitry) can behardware- or software-based (or a combination of both hardware- andsoftware-based). The apparatus can optionally include code that createsan execution environment for computer programs, for example, code thatconstitutes processor firmware, a protocol stack, a database managementsystem, an operating system, or a combination of execution environments.The present disclosure contemplates the use of data processingapparatuses with or without conventional operating systems, such asLINUX, UNIX, WINDOWS, MAC OS, ANDROID, or IOS.

A computer program, which can also be referred to or described as aprogram, software, a software application, a module, a software module,a script, or code, can be written in any form of programming language.Programming languages can include, for example, compiled languages,interpreted languages, declarative languages, or procedural languages.Programs can be deployed in any form, including as stand-alone programs,modules, components, subroutines, or units for use in a computingenvironment. A computer program can, but need not, correspond to a filein a file system. A program can be stored in a portion of a file thatholds other programs or data, for example, one or more scripts stored ina markup language document, in a single file dedicated to the program inquestion, or in multiple coordinated files storing one or more modules,sub-programs, or portions of code. A computer program can be deployedfor execution on one computer or on multiple computers that are located,for example, at one site or distributed across multiple sites that areinterconnected by a communication network. While portions of theprograms illustrated in the various figures may be shown as individualmodules that implement the various features and functionality throughvarious objects, methods, or processes, the programs can instead includea number of sub-modules, third-party services, components, andlibraries. Conversely, the features and functionality of variouscomponents can be combined into single components as appropriate.Thresholds used to make computational determinations can be statically,dynamically, or both statically and dynamically determined.

The methods, processes, or logic flows described in this specificationcan be performed by one or more programmable computers executing one ormore computer programs to perform functions by operating on input dataand generating output. The methods, processes, or logic flows can alsobe performed by, and apparatus can also be implemented as, specialpurpose logic circuitry, for example, a CPU, an FPGA, or an ASIC.

Computers suitable for the execution of a computer program can be basedon one or more of general and special purpose microprocessors and otherkinds of CPUs. The elements of a computer are a CPU for performing orexecuting instructions and one or more memory devices for storinginstructions and data. Generally, a CPU can receive instructions anddata from (and write data to) a memory.

Graphics processing units (GPUs) can also be used in combination withCPUs. The GPUs can provide specialized processing that occurs inparallel to processing performed by CPUs. The specialized processing caninclude artificial intelligence (AI) applications and processing, forexample. GPUs can be used in GPU clusters or in multi-GPU computing.

A computer can include, or be operatively coupled to, one or more massstorage devices for storing data. In some implementations, a computercan receive data from, and transfer data to, the mass storage devicesincluding, for example, magnetic, magneto-optical disks, or opticaldisks. Moreover, a computer can be embedded in another device, forexample, a mobile telephone, a personal digital assistant (PDA), amobile audio or video player, a game console, a global positioningsystem (GPS) receiver, or a portable storage device such as a universalserial bus (USB) flash drive.

Computer-readable media (transitory or non-transitory, as appropriate)suitable for storing computer program instructions and data can includeall forms of permanent/non-permanent and volatile/non-volatile memory,media, and memory devices. Computer-readable media can include, forexample, semiconductor memory devices such as random access memory(RAM), read-only memory (ROM), phase change memory (PRAM), static randomaccess memory (SRAM), dynamic random access memory (DRAM), erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), and flash memory devices.Computer-readable media can also include, for example, magnetic devicessuch as tape, cartridges, cassettes, and internal/removable disks.Computer-readable media can also include magneto-optical disks andoptical memory devices and technologies including, for example, digitalvideo disc (DVD), CD-ROM, DVD+/−R, DVD-RAM, DVD-ROM, HD-DVD, andBLU-RAY. The memory can store various objects or data, including caches,classes, frameworks, applications, modules, backup data, jobs, webpages, web page templates, data structures, database tables,repositories, and dynamic information. Types of objects and data storedin memory can include parameters, variables, algorithms, instructions,rules, constraints, and references. Additionally, the memory can includelogs, policies, security or access data, and reporting files. Theprocessor and the memory can be supplemented by, or incorporated into,special purpose logic circuitry.

Implementations of the subject matter described in the presentdisclosure can be implemented on a computer having a display device forproviding interaction with a user, including displaying information to(and receiving input from) the user. Types of display devices caninclude, for example, a cathode ray tube (CRT), a liquid crystal display(LCD), a light-emitting diode (LED), and a plasma monitor. Displaydevices can include a keyboard and pointing devices including, forexample, a mouse, a trackball, or a trackpad. User input can also beprovided to the computer through the use of a touchscreen, such as atablet computer surface with pressure sensitivity or a multi-touchscreen using capacitive or electric sensing. Other kinds of devices canbe used to provide for interaction with a user, including to receiveuser feedback including, for example, sensory feedback including visualfeedback, auditory feedback, or tactile feedback. Input from the usercan be received in the form of acoustic, speech, or tactile input. Inaddition, a computer can interact with a user by sending documents to,and receiving documents from, a device that the user uses. For example,the computer can send web pages to a web browser on a user's clientdevice in response to requests received from the web browser.

The term “graphical user interface,” or “GUI,” can be used in thesingular or the plural to describe one or more graphical user interfacesand each of the displays of a particular graphical user interface.Therefore, a GUI can represent any graphical user interface, including,but not limited to, a web browser, a touch-screen, or a command lineinterface (CLI) that processes information and efficiently presents theinformation results to the user. In general, a GUI can include aplurality of user interface (UI) elements, some or all associated with aweb browser, such as interactive fields, pull-down lists, and buttons.These and other UI elements can be related to or represent the functionsof the web browser.

Implementations of the subject matter described in this specificationcan be implemented in a computing system that includes a back-endcomponent, for example, as a data server, or that includes a middlewarecomponent, for example, an application server. Moreover, the computingsystem can include a front-end component, for example, a client computerhaving one or both of a graphical user interface or a Web browserthrough which a user can interact with the computer. The components ofthe system can be interconnected by any form or medium of wireline orwireless digital data communication (or a combination of datacommunication) in a communication network. Examples of communicationnetworks include a local area network (LAN), a radio access network(RAN), a metropolitan area network (MAN), a wide area network (WAN),Worldwide Interoperability for Microwave Access (WIMAX), a wirelesslocal area network (WLAN) (for example, using 802.11 a/b/g/n or 802.20or a combination of protocols), all or a portion of the Internet, or anyother communication system or systems at one or more locations (or acombination of communication networks). The network can communicatewith, for example, Internet Protocol (IP) packets, frame relay frames,asynchronous transfer mode (ATM) cells, voice, video, data, or acombination of communication types between network addresses.

The computing system can include clients and servers. A client andserver can generally be remote from each other and can typicallyinteract through a communication network. The relationship of client andserver can arise by virtue of computer programs running on therespective computers and having a client-server relationship.

Cluster file systems can be any file system type accessible frommultiple servers for read and update. Locking or consistency trackingmay not be necessary since the locking of exchange file system can bedone at application layer. Furthermore, Unicode data files can bedifferent from non-Unicode data files.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of what may beclaimed, but rather as descriptions of features that may be specific toparticular implementations. Certain features that are described in thisspecification in the context of separate implementations can also beimplemented, in combination, in a single implementation. Conversely,various features that are described in the context of a singleimplementation can also be implemented in multiple implementations,separately, or in any suitable sub-combination. Moreover, althoughpreviously described features may be described as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can, in some cases, be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

Particular implementations of the subject matter have been described.Other implementations, alterations, and permutations of the describedimplementations are within the scope of the following claims as will beapparent to those skilled in the art. While operations are depicted inthe drawings or claims in a particular order, this should not beunderstood as requiring that such operations be performed in theparticular order shown or in sequential order, or that all illustratedoperations be performed (some operations may be considered optional), toachieve desirable results. In certain circumstances, multitasking orparallel processing (or a combination of multitasking and parallelprocessing) may be advantageous and performed as deemed appropriate.

Moreover, the separation or integration of various system modules andcomponents in the previously described implementations should not beunderstood as requiring such separation or integration in allimplementations. It should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products.

Accordingly, the previously described example implementations do notdefine or constrain the present disclosure. Other changes,substitutions, and alterations are also possible without departing fromthe spirit and scope of the present disclosure.

Furthermore, any claimed implementation is considered to be applicableto at least a computer-implemented method; a non-transitory,computer-readable medium storing computer-readable instructions toperform the computer-implemented method; and a computer system includinga computer memory interoperably coupled with a hardware processorconfigured to perform the computer-implemented method or theinstructions stored on the non-transitory, computer-readable medium.

What is claimed is:
 1. A computer-implemented method, comprising:generating a numerical simulation model based on observed productionrates and flowing pressure and build-up (FPBU) test rates; generating,using simulated FPBU data extracted from the numerical simulation model,a simulated diagnostic plot; adjusting simulation model properties ofthe numerical simulation model until the simulated diagnostic plotmatches within a tolerance to an observed FPBU diagnostic plot;predicting, using the simulated FPBU data, predicted values including astatic pressure, a water cut, and a gas-oil rate (GOR); and reviewingand quality-checking observed data of a reservoir based on comparing thepredicted values within a tolerance of the observed data.
 2. Thecomputer-implemented method of claim 1, wherein the tolerance is definedas 5%.
 3. The computer-implemented method of claim 1, wherein thediagnostic plot is a derivative plot for stabilizing a derivative of afunction of permeability.
 4. The computer-implemented method of claim 1,wherein the diagnostic plot is a delta-pressure plot for fixing thevalue of a well productivity index as a separation between a derivativestabilization and a function of skin.
 5. The computer-implemented methodof claim 1, wherein the diagnostic plot includes a plot for ahistory-matched pessimistic case plus a productivity index (PI)multiplier.
 6. The computer-implemented method of claim 1, wherein thediagnostic plot includes a plot for a history-matched pessimistic caseplus a PI multiplier and a rate constraint.
 7. The computer-implementedmethod of claim 1, wherein the diagnostic plot includes a truth caseforecast and a history-matched case forecast.
 8. A non-transitory,computer-readable medium storing one or more instructions executable bya computer system to perform operations comprising: generating anumerical simulation model based on observed production rates andflowing pressure and build-up (FPBU) test rates; generating, usingsimulated FPBU data extracted from the numerical simulation model, asimulated diagnostic plot; adjusting simulation model properties of thenumerical simulation model until the simulated diagnostic plot matcheswithin a tolerance to an observed FPBU diagnostic plot; predicting,using the simulated FPBU data, predicted values including a staticpressure, a water cut, and a gas-oil rate (GOR); and reviewing andquality-checking observed data of a reservoir based on comparing thepredicted values within a tolerance of the observed data.
 9. Thenon-transitory, computer-readable medium of claim 8, wherein thetolerance is defined as 5%.
 10. The non-transitory, computer-readablemedium of claim 8, wherein the diagnostic plot is a derivative plot forstabilizing a derivative of a function of permeability.
 11. Thenon-transitory, computer-readable medium of claim 8, wherein thediagnostic plot is a delta-pressure plot for fixing the value of a wellproductivity index as a separation between a derivative stabilizationand a function of skin.
 12. The non-transitory, computer-readable mediumof claim 8, wherein the diagnostic plot includes a plot for ahistory-matched pessimistic case plus a productivity index (PI)multiplier.
 13. The non-transitory, computer-readable medium of claim 8,wherein the diagnostic plot includes a plot for a history-matchedpessimistic case plus a PI multiplier and a rate constraint.
 14. Thenon-transitory, computer-readable medium of claim 8, wherein thediagnostic plot includes a truth case forecast and a history-matchedcase forecast.
 15. A computer-implemented system, comprising: one ormore processors; and a non-transitory computer-readable storage mediumcoupled to the one or more processors and storing programminginstructions for execution by the one or more processors, theprogramming instructions instructing the one or more processors toperform operations comprising: generating a numerical simulation modelbased on observed production rates and flowing pressure and build-up(FPBU) test rates; generating, using simulated FPBU data extracted fromthe numerical simulation model, a simulated diagnostic plot; adjustingsimulation model properties of the numerical simulation model until thesimulated diagnostic plot matches within a tolerance to an observed FPBUdiagnostic plot; predicting, using the simulated FPBU data, predictedvalues including a static pressure, a water cut, and a gas-oil rate(GOR); and reviewing and quality-checking observed data of a reservoirbased on comparing the predicted values within a tolerance of theobserved data.
 16. The computer-implemented system of claim 15, whereinthe tolerance is defined as 5%.
 17. The computer-implemented system ofclaim 15, wherein the diagnostic plot is a derivative plot forstabilizing a derivative of a function of permeability.
 18. Thecomputer-implemented system of claim 15, wherein the diagnostic plot isa delta-pressure plot for fixing the value of a well productivity indexas a separation between a derivative stabilization and a function ofskin.
 19. The computer-implemented system of claim 15, wherein thediagnostic plot includes a plot for a history-matched pessimistic caseplus a productivity index (PI) multiplier.
 20. The computer-implementedsystem of claim 15, wherein the diagnostic plot includes a plot for ahistory-matched pessimistic case plus a PI multiplier and a rateconstraint.